During research and development of unmanned vehicles, a lot of real scenarios are needed to test correctness of algorithm.
However, it will be very dangerous and less efficient if all tests are performed in real traffic scenarios. Hence, it is necessary to use simulated traffic scenarios in place of real traffic scenarios to complete a lot of preliminary tests.
In a complicated traffic scenario, there are diverse agents which move freely by certain rules in the complicated traffic scenario.
The agents refer to entities that have an initiative moving capability, and may comprise pedestrians, bicycles, vehicles, cars and the like.
Correspondingly, upon simulating the traffic scenario, it is necessary to simulate a scenario map as well as various agents that might occur in the scenario.
In the prior art, the agents are mainly simulated in the following manners:
Feature information of a series of different agents is pre-defined manually, and simulated agents conforming to the feature information are generated according to manually-selected feature information.
However, this manner has substantial limitation. For example, manually-defined feature information might not comply with real traffic scenario or lacks certain feature information, and thereby affects correctness of subsequent test results of unmanned vehicles.